Law enforcement is one of the many occupational areas where use of technology has had a major impact. One particular application of technology in this area is a general approach called predictive policing. Predictive policing is taking data about crime from many sources, analyzing it, and then using the results of that analysis to combat future crime (Pearsall, 2010). One of the specific applications of predictive policing is a law enforcement management approach called COMPSTAT, where the actions of police are guided by computer statistics. This contrasts with older approaches to law enforcement that relied upon random patrol plans, officer intuition and experience, or purely crime-reactive direction as to where personnel were dispatched (Friend, 2013). Although there is some controversy about what exactly COMPSTAT stands for, a core function of technology in implementing this approach is reflected on it reliance on “data-driven analysis of problems and assessment of department’s problem-solving efforts” (Willis, Mastrofski, & Weisburd, 2003, pp. 2-4). Therefore, COMPSTAT, and other predictive policing programs like that implemented in Santa Cruz and Los Angeles, California, are methods of law enforcement that require solid information technology behind it to function.
Like all information technology processes, COMPSTAT and other predictive police programs can be broken down into input, processing, output, and feedback steps. In particular, COMPSTAT is founded on six core principles that can be categorized into these four basic information-processing steps (Willis, Mastrofski, & Weisburd, 2003, p. 4). The first element is mission clarification; this provides input for the department’s members as to what are the goals being accomplished. The next core element is internal accountability. This is part of the processing step as by upholding accountability, information will be accessed and utilized differently within the department rather than dismissed as not important to the mission. The next element is geographic organization of the operational command. Having a geographic focus on results has aspects for both input and output – as it defines how the information will be collected and put in, as well as the way that the information is communicated to the patrol officers. Figure 1 shows an example of the geographic output of the data that is used from the Santa Cruz predictive policing program with the 500-foot-square hotspots as red boxes. Interestingly, the Santa Cruz department does not require patrolling in the hotspot areas, but only if other duties allow time for it, a decision to maintain patrol autonomy (Friend, 2013).
Organizational flexibility and innovative problem-solving tactics, two additional core elements of COMPSTAT, are both reflective of the processing and output steps as these descriptions represent the desired approaches to how the organization should consider the incoming data and react to that input. But by far the most important core element of the COMPSTAT program is the data-driven analysis of problems and assessment of department efforts mentioned above (Willis, Mastrofski, & Weisburd, 2003, p. 4). This core element touches all aspects of the information technology process as it defines the input, selects the method used for analysis of the input, focuses the output into data-based expressions of likely places where crime would occur, and also defines what kind of feedback should be utilized for assessment, specifically data-based information. It is also the place within the COMPSTAT core elements where emerging technologies, such as computers, algorithms, and databases, find their most important applications. Thus, all six of the core elements of COMPSTAT have significant contributions to the standard input, processing, output, and feedback steps of an information technology process.
A final way of looking at predictive policing is through a SWOT analysis. SWOT stands for strengths, weaknesses, opportunities, and threats and therefore looks at both positive and negative aspects and internal and external considerations of an issue. Looking at predictive policing in this way is illustrative, because it does have its negative possibilities, particularly in the role of privacy rights that might otherwise be overlooking in the zeal to fight crime (Predictive policing, 2013). In particular, the positive aspects to a police force seem to be many. The programs
take information that is routinely collected and utilizes in a way to help focus manpower properly. It also helped to equalize decision-making information between experienced and inexperienced officers. Santa Cruz found that veteran officers could identify about 8 to 9 of the fifteen identified hotspots in advance, just using their experience, while rookie officers could only provide 1 or 2 without the program’s help. Thus, the program helped both veteran and rookie police, but to different extents (Friend, 2013). The importance of making the analysis completely based on data is also a useful aspect of using technology, as crime analysts will have biases like any other profession.
The perceived internal negatives are primarily procedural but still need consideration. The program needs consistent update with information in order to be effective and this be a significant administrative strain, particularly on smaller departments. Using the data in this way is also a significant change in procedure and may be particularly difficult for veterans and management level officers. There is also the possibility that the predictions would become too relied upon and other aspects of police work get improperly ignored. Finally, as with all uses of technology, there is the possibility that its use will be used to support desired labor reductions in an effort to balance budgets.
The external threat of infringing on personal privacy based on a prediction of crime is one addressed in the movie Minority Report. Although that was science fiction, the current predictive policing is the first step in the direction portrayed there and thus should be watched. This should be balanced, of course, with the reductions in crime rate that have already been seen using programs like COMPSTAT. But in any case, there is no doubt that predictive policing does provide a very useful technological addition to today’s police work and is a tool that will likely only become more prevalent into the future.
Figure 1. Example of Geographic Organization of an Output (Friend, 2013)
References
Predictive policing: Don’t even think about it. (2013). The Economist. 20 July. Retrieved from
http://www.economist.com/news/briefing/21582042-it-getting-easier-foresee-wrongdoing-and-spot-likely-wrongdoers-dont-even-think-about-it
Friend, Z. (2013). Predictive policing: Using technology to reduce crime. FBI Law Enforcement Bulletin. Retrieved from
http://www.fbi.gov/stats-services/publications/law-enforcement-bulletin/2013/April/predictive-policing-using-technology-to-reduce-crime
Pearsall, B. (2010). Predictive policing: The future of law enforcement?. National Institute of Justice Journal. 266. Retrieved from
http://www.nij.gov/journals/266/predictive.htm
Willis, J., Mastrofski, S.D., & Weisburd, D. (2003). COMPSTAT in practice: An in-depth analysis of three cities. Police Foundation. Retrieved from
http://www.policefoundation.org/sites/pftest1.drupalgardens.com/files/Willis%20et%20al.%20%282004%29%20-%20Compstat%20in%20Practice.pdf
